Due to the complex structure and diverse operating environments of electromechanical special equipment, it is prone to various faults during operation. When traditional SOM neural networks are applied to fault detection of electromechanical special equipment, due to their fixed network structure and slow convergence speed, the network cannot fully learn fault features during training, and is prone to false alarms and missed alarms during the detection process, resulting in low detection accuracy. A design method for an automatic fault detection system for electromechanical special equipment based on an improved SOM neural network is proposed. In terms of hardware design, the collection and processing components of the operation data of electromechanical special equipment are modified, and the anti-interference processing of the hardware system is achieved through circuit filtering and isolation. Build a database for automatic fault detection system of electromechanical special equipment to ensure the integrity and retrievability of the data. In terms of software design, fault detection standards are set based on the different fault characteristics of electromechanical special equipment to ensure that the system can accurately identify and determine equipment faults. Install the data acquisition components in the hardware system into the special equipment to be tested, achieving automatic collection of target operating data for the special equipment. According to the automatic fault detection requirements of electromechanical special equipment, the structure and working principle of SOM neural network are improved, and the operation data of special equipment is iteratively learned based on the improved algorithm, and the extraction results of special equipment operation characteristics are output. By using feature matching, identify whether the equipment has malfunctioned and the specific type of malfunction, and achieve the automatic fault detection function of the system. From the system test results, it can be seen that the error detection rate and missed detection rate of the optimized design system"s fault state are both below 5%, indicating a higher fault detection accuracy.